Neurostimulator Programming via Patient Profile Matching
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Solution Overview
Problem
Current neurostimulator programming devices lack assistance and recommendations for clinicians, relying on training and experience, and do not leverage collective knowledge to determine effective stimulation programs, leading to inefficiencies and variability in patient outcomes.
Innovation Solution
A system utilizing a shared database of patient profiles and stimulation programs, with an automated programming assistant that matches new patient profiles with similar pre-existing profiles, providing recommended stimulation programs based on anatomical variations and pain maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing programmer devices are used as dumb interfaces relying on clinician training and experience, then clinicians can determine stimulation parameters, but the process is time consuming and success varies across patients and clinicians
Solution Approach 1:
The system performs preliminary actions by pre-processing patient data (pain maps, lead placement, anatomical features) and pre-matching with therapy profiles from the database before the clinician even begins programming. This advance preparation reduces the time required during the actual programming session and improves reliability by presenting pre-vetted options.
Solution Approach 2:
The programmer device acts as an intermediary between the patient's anatomical/physiological characteristics and the stimulation parameters. It introduces a database of pre-existing therapy profiles as a mediator that bridges the gap between individual patient characteristics and effective treatment parameters, reducing variability across different clinicians.
2Ease of operation
If existing programmer devices are used as dumb interfaces, then clinicians have full control over programming, but training of new clinicians is expensive and time-consuming
Solution Approach 1:
The system creates copies of successful therapy profiles from experienced clinicians and stores them in a database. These copied profiles can be adapted and applied to new patients, allowing new clinicians to benefit from the collective knowledge and experience encoded in the database without requiring extensive individual training.
Solution Approach 2:
The programmer device serves multiple functions: it acts as a traditional programming interface, a database management system, a matching algorithm engine, and a knowledge repository. This multi-functionality consolidates collective clinician knowledge into a universal system that assists all clinicians regardless of their individual experience level.
3Adaptability or versatility
If pain maps from multiple patients are not utilized, then individual patient data is protected, but collective programming knowledge cannot be leveraged to promote good programming practices
Solution Approach 1:
The system segments patient data into distinct, matchable features (pain map characteristics, lead placement parameters, anatomical measurements, therapy profile elements). This segmentation allows for systematic comparison and matching while maintaining data organization and protecting individual patient identities through aggregation and anonymization.
Solution Approach 2:
The system transforms individual patient-specific anatomical and physiological parameters into standardized, comparable parameters that can be matched against the database. By changing the representation of patient data into standardized parameters, the system enables collective knowledge leverage while maintaining adaptability to individual patient variations.
Data Source
AI summary
A method and system are provided to assist in programming of a neurostimulator based on a collection of pre-existing therapy profiles. The method and system access a collection of pre-existing therapy profiles derived from prior actual patients or patient models. The pre-existing therapy profiles include stimulation programs mapped to pre-existing patient profiles. The pre-existing patient profiles have at least one of i) prior lead attribute, ii) prior pain maps, and iii) prior stimulation maps for prior patients or models of patients. The method and system further compare the new patient profile with at least a portion of the collection of pre-existing patient profiles to generate profile matching scores indicating an amount of similarity between the pre-existing patient and the new therapy profile.